Decision Making Under Deep Uncertainty With Fuzzy Algorithm in Framework of Multi-Model Approach

2018 ◽  
pp. 1-34
Author(s):  
Yuriy V. Kostyuchenko ◽  
Yulia Stoyka ◽  
Iurii Negoda ◽  
Ivan Kopachevsky
Keyword(s):  
Water Quality ◽  
Risk Management ◽  
Decision Support ◽  
Soft Computing ◽  
Deep Uncertainty ◽  
Fuzzy Algorithm ◽  
Quality Degradation ◽  
Spatially Distributed ◽  
Distributed Measurement ◽  
Model Approach

Task of soft computing for decision support in field of risk management is analyzed in this chapter. Multi-model approach is described. Interrelations between models, remote sensing data and forecasting are described. Method of water quality assessment using satellite observation is described. Method is based on analysis of spectral reflectance of aquifers. Correlations between reflectance and pollutions are quantified. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality is making based on fuzzy algorithm using limited set of uncertain parameters. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated. Conclusions concerned soft computing in risk management are proposed and discussed. It was demonstrated, that basing on spatially distributed measurement data, proposed approach allows to calculate risk parameters with resolution close to observations.

Decision Making Under Deep Uncertainty With Fuzzy Algorithm in Framework of Multi-Model Approach

2019 ◽  
pp. 456-489
Author(s):  
Yuriy V. Kostyuchenko ◽  
Yulia Stoyka ◽  
Iurii Negoda ◽  
Ivan Kopachevsky
Keyword(s):  
Water Quality ◽  
Risk Management ◽  
Decision Support ◽  
Soft Computing ◽  
Deep Uncertainty ◽  
Fuzzy Algorithm ◽  
Quality Degradation ◽  
Spatially Distributed ◽  
Distributed Measurement ◽  
Model Approach

Task of soft computing for decision support in field of risk management is analyzed in this chapter. Multi-model approach is described. Interrelations between models, remote sensing data and forecasting are described. Method of water quality assessment using satellite observation is described. Method is based on analysis of spectral reflectance of aquifers. Correlations between reflectance and pollutions are quantified. Fuzzy logic based approach for decision support in field of water quality degradation risk is discussed. Decision on water quality is making based on fuzzy algorithm using limited set of uncertain parameters. It is shown that this algorithm allows estimate water quality degradation rate and pollution risks. Using proposed approach, maps of surface water pollution risk from point and diffuse sources are calculated. Conclusions concerned soft computing in risk management are proposed and discussed. It was demonstrated, that basing on spatially distributed measurement data, proposed approach allows to calculate risk parameters with resolution close to observations.

scholarly journals A Qualitative Study on the US Forest Service’s Risk Management Assistance Efforts to Improve Wildfire Decision-Making

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 344
Author(s):  
Courtney A. Schultz ◽  
Lauren F. Miller ◽  
Sarah Michelle Greiner ◽  
Chad Kooistra
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Decision Support ◽  
Qualitative Study ◽  
Fire Management ◽  
Perceived Value ◽  
Forest Service ◽  
The Us ◽  
Decision Biases ◽  
Line Officers

To support improved wildfire incident decision-making, in 2017 the US Forest Service (Forest Service) implemented risk-informed tools and processes, together known as Risk Management Assistance (RMA). The Forest Service is developing tools such as RMA to improve wildfire decision-making and implements these tools in complex organizational environments. We assessed the perceived value of RMA and factors that affected its use to inform the literature on decision support for fire management. We sought to answer two questions: (1) What was the perceived value of RMA for line officers who received it?; and (2) What factors affected how RMA was received and used during wildland fire events? We conducted a qualitative study involving semi-structured interviews with decision-makers to understand the contextualized and interrelated factors that affect wildfire decision-making and the uptake of a decision-support intervention such as RMA. We used a thematic coding process to analyze our data according to our questions. RMA increased line officers’ ability to communicate the rationale underlying their decisions more clearly and transparently to their colleagues and partners. Our interviewees generally said that RMA data analytics were valuable but did not lead to changes in their decisions. Line officer personality, pre-season exposure to RMA, local political dynamics and conditions, and decision biases affected the use of RMA. Our findings reveal the complexities of embracing risk management, not only in the context of US federal fire management, but also in other similar emergency management contexts. Attention will need to be paid to existing decision biases, integration of risk management approaches in the interagency context, and the importance of knowledge brokers to connect across internal organizational groups. Our findings contribute to the literature on managing change in public organizations, specifically in emergency decision-making contexts such as fire management.

scholarly journals Modeling of Bank Credit Risk Management Using the Cost Risk Model

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Credit Risk ◽  
Risk Model ◽  
Bank Credit ◽  
Credit Risk Management ◽  
Loan Default ◽  
Increased Risk ◽  
Credit Portfolio ◽  
Cost Risk

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

A decision support system using soft computing for modern international container transportation services

2010 ◽  
Vol 10 (4) ◽  
pp. 1087-1095 ◽  
Author(s):  
Yanbin Liu ◽  
Chunguang Zhou ◽  
Dongwei Guo ◽  
Kangping Wang ◽  
Wei Pang ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Soft Computing ◽  
Support System ◽  
Container Transportation ◽  
Transportation Services

scholarly journals Risk-based principles for defining and managing water security

2013 ◽  
Vol 371 (2002) ◽  
pp. 20120407 ◽  
Author(s):  
Jim Hall ◽  
Edoardo Borgomeo
Keyword(s):  
Water Quality ◽  
Public Policy ◽  
Risk Management ◽  
Water Security ◽  
Water Systems ◽  
Economic Systems ◽  
Water Resources Planning ◽  
Widespread Application ◽  
Trade Offs ◽  
Resources Planning

The concept of water security implies concern about potentially harmful states of coupled human and natural water systems. Those harmful states may be associated with water scarcity (for humans and/or the environment), floods or harmful water quality. The theories and practices of risk analysis and risk management have been developed and elaborated to deal with the uncertain occurrence of harmful events. Yet despite their widespread application in public policy, theories and practices of risk management have well-known limitations, particularly in the context of severe uncertainties and contested values. Here, we seek to explore the boundaries of applicability of risk-based principles as a means of formalizing discussion of water security. Not only do risk concepts have normative appeal, but they also provide an explicit means of addressing the variability that is intrinsic to hydrological, ecological and socio-economic systems. We illustrate the nature of these interconnections with a simulation study, which demonstrates how water resources planning could take more explicit account of epistemic uncertainties, tolerability of risk and the trade-offs in risk among different actors.

scholarly journals Prototype of a Web-based Participative Decision Support Platform in Natural Hazards and Risk Management

2015 ◽  
Vol 4 (3) ◽  
pp. 1201-1224 ◽  
Author(s):  
Zar Aye ◽  
Michel Jaboyedoff ◽  
Marc-Henri Derron ◽  
Cees van Westen
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Natural Hazards ◽  
Web Based
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